Method for editing a multi-point facies simulation

ABSTRACT

A computer system and a hybrid method for combining multipoint statistic and object-based methods include creating a multi-point statistics (MPS) model using a MPS method that satisfies conditioning data and constraints, the multi-point statistics being derived from a training image created using training-image generation parameters; generating one or more object-shape templates and depositional coordinates of each facies type using the parameters; positioning the templates within the MPS model such that the templates maximally correlate to the MPS model; assigning to each of the positioned templates a unique event; determining which cells are available for editing; and assigning the cells that are available for editing to facies if the cells are contained by a facies template positioned within the MPS model at its optimally correlating location.

FIELD

The present invention pertains in general to computation methods andmore particularly to a computer system and computer-implemented methodof combining multipoint statistic and object-based methods for creatingreservoir property models and method for editing a multipoint faciessimulation (MPS).

BACKGROUND

In the characterization of oil fields in the petroleum industry,three-dimensional (3D) modeling using geostatistics is often used toassess reservoir heterogeneity and connectivity. Geostatistics oftenuses kriging to interpolate between data points or conditioning data.Conditioning data includes well log hard data, but can also include softdata, typically seismic data.

Conventional 3D modeling methods are based on variogram ortwo-point-statistics. Variogram-based algorithms allow integrating welland seismic data using a pixel-based approach. First, well data areblocked to the reservoir stratigraphic grid, i.e. well data values areassigned to the cells that the wells penetrate and sample. Then, allunsampled cells in the reservoir stratigraphic grid are simulatedconditional to well and seismic data using some form of kriging.However, the models built using conventional variogram-based methods aremost often not consistent with geological interpretation.Variogram-based geostatistics is inadequate in integrating geologicalconcepts: two-point statistics variograms do not allow modeling complexgeological heterogeneity. As a result, the variogram-based methodsusually generate models that provide poor reservoir performanceforecasting.

Over the past 10 years, the traditional variogram-based methods havebeen replaced by Multiple Point Statistics (MPS) methods. The MPSapproach replaces traditional variograms with 3D numerical conceptualmodels of the subsurface geology, also known as training images.

MPS simulation is a reservoir facies modeling technique that usesconceptual geological models as 3D training images (or training cubes)to generate geologically realistic reservoir models. The training imagesprovide a conceptual description of the subsurface geological geobodies,based on well log interpretation and general experience in reservoirarchitecture modeling. MPS simulation extracts multiple-point patternsfrom the training image and anchors the patterns to reservoir well data.A 3D data template is provided by a user to define the dimensions of themulti-point patterns to be reproduced from the training image.Specifically, a size of the 3D data template corresponds to the maximumnumber of conditioning data used to infer statistics from the trainingimage during the MPS simulation process.

Another facies modeling technique is the object-based modeling (alsoreferred to as Boolean modeling) technique. Object-based modeling is amethod that uses and distributes quantifiable 3D facies geometries orshapes in an earth model. In the object-based modeling method, a varietyof predefines 3D geological shapes, such as polygonal shapes,cylindrical shapes or more complex shapes, are used to modeldistribution of facies in an earth model.

Both multi-point statistics (MPS) and object-based modeling haveadvanced the state-of-the-art in geostatistical facies-based propertymodeling to build geocellular models for reservoir simulation. MPS hasthe benefit that it can far more easily match conditioning facies datawith well data. Object-based modeling has the benefit that“depositional” property trends (such as sedimentary deposits) can beplaced within the objects that follow the boundaries of the objects in away that resembles true sedimentary deposits.

However, none of the conventional methods achieves the desired result increating a facies-based reservoir model that can match conditioningfacies data with well data as well as provide the ability to placedepositional trends within boundaries of objects to simulate truesedimentary deposits. Furthermore, none of the conventional methods arecapable of reproducing large scale facies continuity that is present intraining images. Therefore, there is a need for methods that cure theabove and other deficiencies of conventional MPS and object-basedmethodologies.

SUMMARY

An aspect of the present invention is to provide a computer-implementedhybrid method for combining multipoint statistic and object-basedmethods. The hybrid method includes creating, using a computer system, amulti-point statistics (MPS) model using a MPS method that satisfiesconditioning data and constraints in which the multi-point statisticsare derived from a training image created using training-imagegeneration parameters; and generating, using the computer system, one ormore object-shape templates of a 2D or 3D object-shape and depositionalor structural coordinates of each facies type using the training imagegeneration parameters. The hybrid method further includes positioning,using the computer system, the one or more generated object-shapetemplates within the MPS model such that the one or more generatedobject-shape templates maximally correlate to the MPS model; andassigning, using the computer system, to each of the one or morepositioned object-shape templates a unique event reference and assigningthe same unique event reference to cells within or in the vicinity ofeach corresponding object-shape template. The hybrid method alsoincludes providing, using the computer system, depositional orstructural coordinates to each cell associated with a given event numberin the MPS model using a relative position of the cell within the objectassociated with the event number; and modeling properties using thedepositional or structural coordinates, using the computer system, tocapture geological trends within each object-shape template.

A further aspect of the present invention is to provide a computersystem for implementing a hybrid method for combining multipointstatistic and object-based methods. The computer system includes acomputer readable memory configured to store input data comprisingconditioning data, constraints, and training-image generationparameters. The computer system further includes a processor configuredto read input data including the conditioning data and constraints andthe training-image generation parameters to: (a) create a multi-pointstatistics (MPS) model using a MPS method that satisfies theconditioning data and the constraints in which the multi-pointstatistics are derived from a training image created using thetraining-image generation parameter; (b) generate one or moreobject-shape templates of a 2D or 3D object-shape and depositional orstructural coordinates of each facies type using the training imagegeneration parameters; (c) position the one or more generatedobject-shape templates within the MPS model such that the one or moregenerated object-shape templates maximally correlate to the MPS model;(d) assign to each of the one or more positioned object-shape templatesa unique event reference and assigning the same unique event referenceto cells within or in the vicinity of each corresponding object-shapetemplate; (e) provide depositional or structural coordinates to eachcell associated with a given event number in the MPS model using arelative position of the cell within the object associated with theevent number; and (f) model properties using the depositional orstructural coordinates to capture geological trends within eachobject-shape template.

Another aspect of the present invention is to provide acomputer-implemented hybrid method for combining multipoint statisticand object-based methods. The hybrid method includes creating, using acomputer system, a multi-point statistics (MPS) model using a MPS methodthat satisfies conditioning data and constraints in which themulti-point statistics are derived from a training image created usingtraining-image generation parameters; and generating, using the computersystem, one or more object-shape templates of a 2D or 3D object-shapeand depositional or structural coordinates of each facies type using thetraining image generation parameters. The method further includespositioning, using the computer system, the one or more generatedobject-shape templates within the MPS model such that the one or moregenerated object-shape templates maximally correlate to the MPS model;and assigning, using the computer system, to each of the one or morepositioned object-shape templates a unique event reference and assigningthe same unique event reference to cells within or in the vicinity ofeach corresponding object-shape template. The method also includesdetermining, using the computer system, which cells are to be leftunedited and which cells are available for editing; and assigning, usingthe computer system, the cells that are available for editing to faciesif the cells that are available for editing are contained by a faciesobject-shape template positioned within the MPS model at its optimallycorrelating location.

A further aspect of the present invention is to provide a computersystem for implementing a hybrid method for combining multipointstatistic and object-based methods. The computer system includes acomputer readable memory configured to store input data comprisingconditioning data, constraints, and training-image generationparameters. The computer system further includes a processor configuredto read input data including the conditioning data and constraints andthe training-image generation parameters to: (a) create a multi-pointstatistics (MPS) model using a MPS method that satisfies conditioningdata and constraints in which the multi-point statistics are derivedfrom a training image created using training-image generationparameters; (b) generate one or more object-shape templates of a 2D or3D object-shape and depositional or structural coordinates of eachfacies type using the training image generation parameters; (c) positionthe one or more generated object-shape templates within the MPS modelsuch that the one or more generated object-shape templates maximallycorrelate to the MPS model; (d) assign to each of the one or morepositioned object-shape templates a unique event reference and assigningthe same unique event reference to cells within or in the vicinity ofeach corresponding object-shape template; (e) determine which cells areto be left unedited and which cells are available for editing; and (f)assign the cells that are available for editing to facies if the cellsthat are available for editing are contained by a facies object-shapetemplate positioned within the MPS model at its optimally correlatinglocation.

Although the various steps of the method according to one embodiment ofthe invention are described in the above paragraphs as occurring in acertain order, the present application is not bound by the order inwhich the various steps occur. In fact, in alternative embodiments, thevarious steps can be executed in an order different from the orderdescribed above or otherwise herein.

These and other objects, features, and characteristics of the presentinvention, as well as the methods of operation and functions of therelated elements of structure and the combination of parts and economiesof manufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of “a”, “an”,and “the” include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings:

FIG. 1 is a flow chart of a hybrid method of combining multipointstatistic and object-based methods for creating reservoir propertymodels suitable for reservoir flow simulation, according to anembodiment of the present invention;

FIG. 2 is a flow diagram showing a convolving procedure for convolvingthe object-shape templates with the MPS model, according to anembodiment of the present invention;

FIG. 3 is a flow diagram of a shape identification process, according toan embodiment of the present invention;

FIG. 4 depicts a training image in an MPS model;

FIG. 5 depicts a property model within the MPS model shown in FIG. 4without using the hybrid method described herein;

FIG. 6 depicts a slice of a training image created using training imagegeneration parameters in an MPS model;

FIG. 7 depicts a property trend model within the slice of the trainingimage shown in FIG. 6 after applying the hybrid method described herein;

FIG. 8 is a flow diagram of a method for editing an MPS model, accordingto an embodiment of the present invention;

FIG. 9 is a flow diagram of a method for editing an MPS model, accordingto an embodiment of the present invention;

FIG. 10 depicts a 2D slice of a 3D training image which containsconnected straight channels;

FIG. 11 depicts a 2D conventional MPS simulation based on the 2D sliceof the 3D training image;

FIG. 12 depicts a 2D slice of an input MPS model, the 2D slice of theinput MPS model includes disconnected channels;

FIG. 13 depicts a 2D slice of an edited MPS model obtained using theediting method described herein; and

FIG. 14 is a schematic diagram representing a computer system forimplementing the methods described herein, according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

FIG. 1 is a flow chart of a hybrid method of combining multipointstatistic and object-based methods for creating reservoir propertymodels suitable for reservoir flow simulation, according to anembodiment of the present invention. The method includes creating amulti-point statistics (MPS) model, at S10. The MPS model can be createdusing any MPS method that satisfies conditioning data and constraints inwhich the multi-point statistics are derived from training imagescreated using training-image generation parameters. Training imagegeneration parameters include object shapes orientation, position, size,etc. Examples of shapes include a triangle, a square, a rectangle, asemi-circle, a ribbon, or more complex shapes.

In one embodiment, an MPS method may include building a grid in adesired geological region and interpreting facies at specific cellswithin the grid (the MPS grid). The method also includes building orgenerating a MPS training image (i.e., a 3D training image or trainingcube). The MPS training image can be built as a collection of faciespatterns that contain no absolute or relative spatial information, i.e.,the training image is not conditioned to well data. For example,training images can be generated, for example, using information fromaerial photography, pictures of outcrops, hand-drawn sketches, etc.

In one embodiment, one method used to build MPS training images includesgenerating unconditional object-based models. First, dimensions, shapes,and orientations of each facies are described and then associationsamong various facies (sand, shale, clay, etc.) are specified. Next,geometry constraints (such as azimuth angle, object size, etc.) andproportion constraints (i.e., facies proportion map or facies proportioncurves) of the various facies within the MPS training images can bespecified.

The MPS modeling method further includes performing an MPS simulation toobtain a simulated image showing the facies model. MPS simulation infersat each cell of the reservoir geological grid the local faciesprobabilities (i.e. the probability that each facies exists at the celllocation) given the conditioning data closest to the cell, and thendrawing a facies value from these probabilities using a Monte-Carlomethod. The local facies probabilities are inferred by looking in theMPS training image for all the patterns that match the conditioningdata. Therefore, an MPS simulation includes assigning facies to eachpixel within each cell of the reservoir geological grid.

The computation of local facies probabilities includes counting thenumber of times patterns similar to the conditioning data, i.e.,patterns that have the same geometrical configuration and same datavalues as the conditioning data, can be found in the MPS training image.In another embodiment, instead of repeatedly scanning the training imagefor each cell to be simulated, all the patterns present in the trainingimage are stored, prior to the simulation, in a dynamic data structuretable called search tree. Only patterns that actually occur over the MPStraining image are stored in the search tree. A data template is definedto limit the geometric extent of these patterns. The size of this datatemplate corresponds to the maximum number of conditioning data thatwill be used to simulate each unsampled cells. The search tree is storedin a storage device or memory. The search tree is organized to allow arelatively fast retrieval of any particular pattern, thus a relativelyfast computation of facies probabilities given any particularconditioning pattern. An example MPS method can be found in U.S. patentapplication Ser. No. 13/493,062 entitled “SYSTEM AND METHOD FOROPTIMIZING THE NUMBER OF CONDITIONING DATA IN MULTIPLE POINT STATISTICSSIMULATION” by Sebastien B. Strebelle et al., the entire contents ofwhich is incorporated herein by reference.

The method further includes generating one or more (for example,multiple) 3D object-shape and depositional or structural coordinates ofeach facies type (also referred to as one or more object-shapetemplates) using training image generation parameters, at S12. The term“depositional coordinates” which relates to the deposition of facies ina geological sense is used herein for illustration purposes only and isnot intended to be limiting. As it can be appreciated other types ofcoordinates can be employed such as more generally a structuralcoordinate related to the structure of the facies. Training imagegeneration parameters include object shapes, orientation, position,size, etc. Similar to the MPS simulation, examples of shapes include atriangle, a square, a rectangle, a semi-circle, a ribbon, or morecomplex shapes. Therefore, there may be a collection of object-shapesthat may include rectangles with different sizes and orientation,triangles with different sizes and orientation and ribbons of differentsizes and orientation, etc. Each of object shapes (with an orientationand size) can be represented as a digital cube of pixels. It is notedthat the object-shape templates associated with the generating of anobject-based training image can be the same or different from theobject-shapes associated with the MPS training image in the MPSsimulation.

If training images are generated with more than one object, only objectsthat are isolated from other objects are used as object-shape templates.Training images generated with only one object can enhance theefficiency of creating object-shape templates. In one embodiment, whenapplying object-shape templates to a grid, the object-shape templatesthat intersect a grid boundary are generally not used. However, theobject-shape templates that intersect a grid boundary may be used insome circumstances. For example, if a vast majority of objects generatedwithin a grid having a same size as the a grid of an MPS model andhaving the specified training-image parameters intersect a specific gridboundary, then intersection of the object-shape template with thatspecific grid boundary is not rejected. For example, long north-southoriented channel objects always intersect the north and south boundariesof the training image and MPS grid. Hence, intersection with theseboundaries is not used as a rejection criterion to reject the objectsintersecting the boundaries.

In one embodiment, the specified parameters for object-shape templategeneration (i.e., the shape of the object, the size of the object, theorientation of the object, the position of the object, etc.) can be thesame as the parameters used for MPS facies simulation but can also bedifferent.

The method further includes positioning the generated object-shapetemplates and copies of the generated object-shape templates within theMPS model such that generated object-shape templates maximally correlateto the MPS simulation, at S14. This can be accomplished using anyalgorithm for pattern recognition. The method further includes assigningto each such positioned object-shape template a unique event reference,such as for example a unique event number, and the cells within theobject-shape template are also assigned the same unique event number, atS16. In the following paragraphs, the term “event number” is employedfor illustration purposes without any intention to limit to only “anumber.” Indeed, as it can be appreciated any type of “event reference”can be used including a letter, a tag, a flag, an indicium, or a number,or any combination thereof. The method may further include assigning thesame event number to adjacent or neighboring cells that do not have anevent number, at S18.

In one embodiment, the positioning may include convolving theobject-shape templates with the MPS model to produce a convolution scorevolume for each object-shape template. The convolution score can benormalized by its standard deviation. Local maximum of the convolutionscore are candidate positions to position object-shapes within the MPSmodel.

For example, a first object-shape template can be convolved with the MPSmodel to produce a first convolution score volume. The first convolutionscore can be normalized to obtain a first normalized convolution scoreand a local maximum of the first normalized convolution score can bedetermined. Similarly, a second object-shape volume template can beconvolved with the same MPS model to produce a second convolution scorevolume. The second convolution score can be normalized to obtain asecond normalized convolution score and a local maximum of the secondnormalized convolution score can be determined. The method furtherincludes comparing the local maximum of normalized convolution scores(e.g., the first normalized score and the second normalized score)produced with one object-shape template (e.g., the first object-shapetemplate) versus another object-shape template (e.g., the secondobject-shape template) to determine which object-shape (e.g., the firstobject-shape or the second object-shape) is a better fit to the MPSmodel.

In one embodiment, an approximation of this process uses a twodimensional (2D) convolution and adds the third dimension at a laterstage. FIG. 2 is a flow diagram showing a convolving procedure forconvolving the object-shape templates with the MPS model, according toan embodiment of the present invention. In one embodiment, theconvolving procedure is performed using 2D object-shape templatesinstead of 3D object-shape templates. The third dimension is added afterperforming the convolution. For example, by using 2D object-shapetemplates instead of 3D object-shape templates a faster convolutionprocess can be achieved. In one embodiment, in order to obtain the 2Dobject-shape templates, the 3D object shape templates are projected ontoa map-view slice to create a 2D object “shadows,” at S30. The objectshadows are convolved with each map-slice of the MPS model, at S32. Theconvolution can be performed with or without using Fast FourierTransform (FFT) methods. However, the convolution can be performedfaster when using FFT methods.

After performing the convolution in the 2D space using 2D object-shapetemplates, convolution scores are normalized by the standard deviationin the volume, at S34. The maximum convolution score (MaxScore) isdetermined for each facies type (e.g., sand, shale, etc.) in the MPSmodel, across all object-shape templates (e.g., the first object-shapetemplate and the second object-shape template), at S36. For example, forsand facies type, a maximum convolution score is determined from aconvolution score obtained when using the first object-shape templateand a convolution score obtained when using the second object-shapetemplate. Similarly, for shale facies type, a maximum convolution scoreis also determined from a convolution score obtained when using thefirst object-shape template and a convolution score obtained when usingthe second object-shape template.

The method next identifies object shapes within the MPS model that bestmatch each facies (e.g., sand, shale, etc.) by using the 2D or 3Dnormalized convolution scores. The first matched object is recorded asevent number one. The second matched object is recorded as event number2, etc.

FIG. 3 is a flow diagram of a shape identification process, according toan embodiment of the present invention. In one embodiment, the shapeidentification process is run for a series of decreasing scorethresholds, T₁, T₂ . . . T_(n). The procedure includes initializing theevent number E to 1 (i.e., E=1), at S40. When cells in the MPS model areassigned an event number they are considered “labeled.” Next, for eachthreshold T₁, T₂ . . . T₁, the following steps are performed:

-   -   i) Determine which cell has the maximum score and its        corresponding object shape template, for all cells in the MPS        model of the given facies that are not yet labeled with an        object event number, at S42.    -   ii) Position a centroid of the object shape template at this        cell, at S44.    -   iii) If 2D rather than 3D convolutions scores are used then: (a)        count the number of unlabeled cells of the given facies that        fall within the template, and (b) move the centroid of the        object shape template up and down to determine the position that        maximizes the cell count in the preceeding step, at S46.    -   iv) Label all unlabeled cells within the object shape template        when its centroid is positioned as in step ii) and iii) with the        event number E and increment the event number, i.e., E=E+1. The        object shape template depositional or structural coordinates are        copied to each such cell, at S48.    -   v) Iterate steps i) to iv) until there are no unlabled cells        with scores above the current score threshold T_(n), at S50.    -   vi) Propagate the event number and depositional or structural        coordinates in the labeled cells to neighboring cells which in        turn propagate to their neighbors, etc. through a recursive        procedure, at S52.

In one embodiment, the propagation is limited to cells that share facesexcept no propagation is done in the downward direction. This form ofpropagation is adapted to preserve object shape at the base ofdepositional objects. The propagation is limited to cells that have anormalized convolution score above a given propagation thresholdPthresh. In one embodiment, the threshold Pthresh is related to thescore threshold T_(n) by the following equation (1).

Pthresh=T _(n) *T _(n)/MaxScore  (1)

This recursive propagation procedure is finished when all unlabeledneighboring cells are either of a different facies or have convolutionscores below Pthresh. Optionally, a Pthresh in the last iteration can beset to 0 to allow labeling of all MPS cells assigned a non-backgroundfacies.

The method may further comprise, iteratively, performing the positioningof the object templates and the assignment of an event number in which aset of best-fitting object shape templates are positioned and propagatedto nearby cells, followed by a set of less well-fitting objects, etc.,at S20. The method further includes providing depositional or structuralcoordinates to each model cell of a given event number using itsrelative position within the object of this event number, at S22.

The method further comprises modeling properties using depositional orstructural coordinates to capture geologic trends within eachobject-shape template, at S24. In one embodiment this can be performedby creating property trends in a process that is well known from object(Boolean) modeling by using the depositional or structural coordinatesto capture geological trends within each object. In one embodiment, thecoordinates include distance in the transverse direction from the objectaxis, distance along the long axis of each object, or vertical distancefrom the base of the object, or any combination thereof. Property trendscan be assigned either conceptually or empirically to these threecoordinates such as: a) increasing clay content from an object's axistowards its margins, b) increasing clay content from proximal to distalportions, or c) increasing clay content from basal layers to top layers.

FIG. 4 depicts a training image in an MPS model. FIG. 5 depicts aproperty model within the MPS model shown in FIG. 4 without using thehybrid method described herein. The property model does not geologicallyfollow object shapes even when anisotropy variograms are used.

FIG. 6 depicts a slice of a training image created using training imagegeneration parameters in an MPS model. FIG. 7 depicts a property trendmodel within the slice of training image shown in FIG. 6 after applyingthe hybrid method described herein. The property trend model is assignedusing depositional or structural transverse coordinates. As shown inFIG. 7, the property trends follow object shapes and dimensions in ageologically reasonable way.

As it can be appreciated from the above paragraphs, the hybrid methodprovides a method for assigning each cell in a MPS model (except forbackground facies cells) to a specific object shape template which isonly partially preserved in the MPS model. In the following paragraphs,a method for editing the MPS model in such a way to greatly increase thepreservation of the object shape templates within the model and therebysignificantly increase the facies continuity of the resulting model isdescribed. One result of performing the editing method is that theedited MPS model qualitatively and quantitatively is more similar to thetraining image.

FIG. 8 is a flow diagram of a method for editing an MPS model, accordingto an embodiment of the present invention. In one embodiment, the methodfor editing the MPS model includes providing a hybrid MPS-Boolean modelwith facies at each cell in a region of interest, at S60. The method mayfurther include providing background facies numbers, faciesprobabilities at each cell in the region of interest, hard-datalocations, and optionally providing target facies proportions,object-shape templates, object event number assignments at each cell inthe region of interest, depositional or structural coordinateassignments at each cell in the region of interest, at S62.

The method further includes inputting parameters. In one embodiment, theinputting includes ordering non-background facies such that in orderedfacies O₁, O₂, O_(n), facies O_(n) is expected to have greatercontinuity than facies O_(n-1), at S64. In one embodiment, the inputtingfurther includes leaving the maximum facies probability unmodified(MaxUnModified) in the ordering of the facies.

The method further includes, for each facies F taken in the order O₁,O₂, . . . O_(n): (a) edit all model cells not at hard-data location orconditioning data locations (i.e., outside hard-data locations) and inwhich the relative facies probability for facies F is greater than themaximum facies probability MaxUnModified, at S66.

The method further includes (i) assigning the model cells to facies F ifthe edited cells are contained by a facies object shape templatepositioned within the model at its optimally correlating location; and(ii) assigning depositional or structural coordinates relative to thecentroid of the object shape template found in the previous step (i), atS68. If more than one object shape template contains the cell, theobject shape template which has the highest correlation to the uneditedMPS model can be used.

The method further includes, for each facies F taken in the order O₁,O₂, . . . O_(n), optionally, only assign a limited number ofobject-shape templates where the limited number of object-shapetemplates allows matching target facies proportion, at S70.

The method further includes, for each facies F taken in the order O₁,O₂, . . . O_(n), optionally, only assign a limited part of eachobject-shape template where the limited part of each object-shapetemplate allows matching target facies proportion, at S72. In oneembodiment, only the basal part of objects can be used to create arealistic geologic facies pattern.

FIG. 9 is a flow diagram of a method for editing an MPS model, accordingto an embodiment of the present invention. In one embodiment, theediting method includes creating a MPS model using any MPS method, atS80. In one embodiment, the editing method includes assigning each cellin the MPS model (except for background facies cells) to a specificobject shape template positioned within the model at its optimallycorrelating location as described in the above MPS-Boolean hybridmethod, at S82.

The editing method further includes assigning each cell in the MPS model(except for background facies cells) to an object event number whichstarts at an index of one for each facies, as described in the abovehybrid method, at S84. Lower objects event numbers indicate objects thatbetter fit the MPS facies simulation.

The editing method further includes obtaining a facies probability cubeby: (a) using the probability cube used in generating the MPS model; or(b) using a 3D smoothing filter on a presence/absence flag in each cellfor each facies, at S86.

The editing method further includes inputting parameters. The inputtingof parameters include ordering the facies such that in the orderedfacies O₁, O₂, O_(n) the facies O_(n) is expected to have greatercontinuity than facies O_(n-1), i.e., increasing continuity, at S88;and, the inputting further includes leaving the maximum faciesprobability unmodified (MaxUnModified) in the ordering of the facies.

The editing method further includes calculating relative faciesprobabilities or target facies proportions from facies probabilities bydividing by the maximum probability for each facies or using any othersuitable norm, at S90. Alternatively, the relative facies probabilitiescan be obtained as an input constraint or calculated from the originalinput MPS model. However, there are various constraints to decidewhether or not to change a cell from one facies to another. One of theconstraints can be, for example, a proportion of the different faciesare to be kept the same, as will be explained further in detail in thefollowing paragraphs. However, other types of constraints can also beimplemented.

The editing method includes making a list of cell locations that containhard data (e.g., well conditioning data).

In one embodiment, the editing method includes setting the edited MPSmodel to the original MPS model, at S92, and, for each facies F taken inthe order O₁, O₂, . . . O_(n), performing the following procedure ofassigning cells to a facies and assigning to the cells depositional orstructural coordinates, at S94:

-   -   (a) Visiting all model cells that are not hard-data cells and        are different from facies F and in which the relative facies        probability is greater than MaxUnModified;    -   (b) Assigning these cells to facies F if these cells are        contained by a facies object-shape template positioned within        the model at its optimally correlating location, as described in        the above paragraphs;    -   (c) Assigning to these cells depositional or structural        coordinates relative to the centroid of the object shape        template found previously. If more than one object shape        template contains the cell, the object shape template with the        lowest event number can be used;

In one embodiment, the editing method further includes, if matchingtarget facies proportions is not required, the editing process iscomplete. Otherwise, if matching target facies proportions is needed,for each facies F, performing the following procedure:

-   -   (a) Creating a list of events with hard data (F) of length        (NEventsWithHardData(F)) of all object event numbers that are        assigned to model cells that contain hard-data;    -   (b) Calculating a total number of required cells of facies F in        the final model to match the target facies proportion        (NObjectCells(F));    -   (c) Calculating an additional number of cells (NTargetCells(F))        to be assigned to facies F using the following equation (2).        That is, the additional number of cells (NTargetCells(F)) being        equal to a difference between the target facies proportion        (NObjectCells(F)) and a length of the list of events with hard        data NEventsWithHardData(F).

NTargetCells(F)=NObjectCells(F)−NEventsWithHardData(F)  (2)

-   -   (d) If the additional number of cells NTargetCells(F) is equal        to or greater than zero (i.e., NTargetCells(F)=or >0),        performing the following procedure.        -   (i) Calculating the number of cells assigned to each object            event number (which is not in the list EventsWithHardData);        -   (ii) Calculating a cumulative number of cells as a function            of event number; and        -   (iii) Finding the event number cutoff (EventNumberCutoff(F))            such that the cumulative number of cells with event number            less than the event number cutoff (EventNumberCutoff(F)) is            approximately equal to the additional number of cells            (NTargetCells(F)).    -   (e) Else, if the additional number of cells (NTargetCells(F)) is        less than zero (NTargetCells(F)<0), then:        -   (i) Calculating the cumulative number of cells assigned to F            as a function of the vertical depositional coordinate (a            vertical coordinate of 0 is at the base of the object); and        -   (ii) Finding the vertical depositional coordinate cutoff            (VerticalDepositionalCoordinateCutoff(F)) such that the            cumulative number of cells with vertical depositional            coordinates less than the vertical depositional cutoff            (VerticalDepositionalCoordinateCutoff (F)) is approximately            equal to the additional number of cells (NTargetCells(F)).

In one embodiment, the editing method may further include repeating, foreach facies F taken in the order O₁ . . . O_(n), visiting all modelcells with the additional cutoff constraints of event number cutoff(EventNumberCutoff(F)) or vertical depositional coordinate cutoff(VerticalDepositionalCoordinateCutoff (F)).

FIG. 10 depicts a 2D slice of a 3D training image which containsconnected straight channels 100. FIG. 11 depicts a 2D conventional MPSsimulation based on the 2D slice of the 3D training image. As shown inFIG. 11, the conventional MPS simulation generates shorter disconnectedchannels 102. Therefore, conventional MPS simulation has difficultyreproducing large scale facies continuity that is present in trainingimages.

FIG. 12 depicts a 2D slice of an input MPS model. The 2D slice of theinput MPS model includes disconnected channels 104. FIG. 13 depicts a 2Dslice of an edited MPS model obtained using the editing method describedherein. As shown in FIG. 13, the 2D slice of the edited MPS modelincludes continuous channels 106. The editing method enhances thecontinuity and object shape reproduction which increases similaritytraining images.

In one embodiment, the method or methods described above can beimplemented as a series of instructions which can be executed by acomputer. As it can be appreciated, the term “computer” is used hereinto encompass any type of computing system or device including a personalcomputer (e.g., a desktop computer, a laptop computer, or any otherhandheld computing device), or a mainframe computer (e.g., an IBMmainframe), or a supercomputer (e.g., a CRAY computer), or a pluralityof networked computers in a distributed computing environment.

For example, the method(s) may be implemented as a software programapplication which can be stored in a computer readable medium such ashard disks, CDROMs, optical disks, DVDs, magnetic optical disks, RAMs,EPROMs, EEPROMs, magnetic or optical cards, flash cards (e.g., a USBflash card), PCMCIA memory cards, smart cards, or other media.

Alternatively, a portion or the whole software program product can bedownloaded from a remote computer or server via a network such as theinternet, an ATM network, a wide area network (WAN) or a local areanetwork.

Alternatively, instead or in addition to implementing the method ascomputer program product(s) (e.g., as software products) embodied in acomputer, the method can be implemented as hardware in which for examplean application specific integrated circuit (ASIC) can be designed toimplement the method.

FIG. 14 is a schematic diagram representing a computer system 110 forimplementing the methods, according to an embodiment of the presentinvention. As shown in FIG. 14, computer system 110 comprises aprocessor (e.g., one or more processors) 120 and a memory 130 incommunication with the processor 120. The computer system 110 mayfurther include an input device 140 for inputting data (such askeyboard, a mouse or the like) and an output device 150 such as adisplay device for displaying results of the computation

As can be appreciated from the above description, the computer readablememory 130 can be configured to store input data comprising conditioningdata and constraints and training-image generation parameters. Theprocessor 120 can be configured to read input data including theconditioning data and constraints and the training-image generationparameters to: (1) create a multi-point statistics (MPS) model using aMPS method that satisfies the conditioning data and the constraints inwhich the multi-point statistics are derived from a training imagecreated using the training-image generation parameter; (2) generate oneor more object-shape templates of a 2D or 3D object-shape anddepositional or structural coordinates of each facies type using thetraining image generation parameters; (3) position the one or moregenerated object-shape templates within the MPS model such that the oneor more generated object-shape templates maximally correlate to the MPSmodel; (4) assign to each of the one or more positioned object-shapetemplates a unique event reference and assigning the same unique eventreference to cells within or in the vicinity of each correspondingobject-shape template; (5) provide depositional or structuralcoordinates to each cell associated with a given event number in the MPSmodel using a relative position of the cell within the object associatedwith the event number; and (6) model properties using the depositionalor structural coordinates to capture geological trends within eachobject-shape template.

In addition, as it can be further appreciated from the abovedescription, the computer readable memory 130 can be configured to storeinput data comprising conditioning data and constraints andtraining-image generation parameters. The processor 120 can beconfigured to read input data including the conditioning data andconstraints and the training-image generation parameters to: (1) createa multi-point statistics (MPS) model using a MPS method that satisfiesconditioning data and constraints in which the multi-point statisticsare derived from a training image created using training-imagegeneration parameters; (2) generate one or more object-shape templatesof a 2D or 3D object-shape and depositional or structural coordinates ofeach facies type using the training image generation parameters; (3)position the one or more generated object-shape templates within the MPSmodel such that the one or more generated object-shape templatesmaximally correlate to the MPS model; (4) assign to each of the one ormore positioned object-shape templates a unique event reference andassigning the same unique event reference to cells within or in thevicinity of each corresponding object-shape template; (5) determinewhich cells are to be left unedited and which cells are available forediting; and (6) assign the cells that are available for editing tofacies if the cells that are available for editing are contained by afacies object-shape template positioned within the MPS model at itsoptimally correlating location.

Although the invention has been described in detail for the purpose ofillustration based on what is currently considered to be the mostpractical and preferred embodiments, it is to be understood that suchdetail is solely for that purpose and that the invention is not limitedto the disclosed embodiments, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present invention contemplates that, to the extent possible, one ormore features of any embodiment can be combined with one or morefeatures of any other embodiment.

Furthermore, since numerous modifications and changes will readily occurto those of skill in the art, it is not desired to limit the inventionto the exact construction and operation described herein. Accordingly,all suitable modifications and equivalents should be considered asfalling within the spirit and scope of the invention.

What is claimed is:
 1. A computer implemented hybrid method forcombining multipoint statistic and object-based methods comprising:creating, using a computer system, a multi-point statistics (MPS) modelusing a MPS method that satisfies conditioning data and constraints inwhich the multi-point statistics are derived from a training imagecreated using training-image generation parameters; generating, usingthe computer system, one or more object-shape templates of a 2D or 3Dobject-shape and depositional or structural coordinates of each faciestype using the training image generation parameters; positioning, usingthe computer system, the one or more generated object-shape templateswithin the MPS model such that the one or more generated object-shapetemplates maximally correlate to the MPS model; assigning, using thecomputer system, to each of the one or more positioned object-shapetemplates a unique event reference and assigning the same unique eventreference to cells within or in the vicinity of each correspondingobject-shape template; determining, using the computer system, whichcells are to be left unedited and which cells are available for editing;assigning, using the computer system, the cells that are available forediting to facies if the cells that are available for editing arecontained by a facies object-shape template positioned within the MPSmodel at its optimally correlating location.
 2. The method according toclaim 1, wherein assigning the cells that are available for editing tofacies comprises, for each facies taken in the order of increasingcontinuity, only assigning a limited number of object-shape templateswhere the limited number of object-shape templates allows matchingtarget facies proportion.
 3. The method according to claim 1, whereinassigning the cells that are available for editing to facies comprises,for each facies taken in the order of increasing continuity, only assigna limited part of each object-shape template where the limited part ofeach object-shape template allows matching target facies proportion. 4.The method according to claim 1, wherein assigning to each of the one ormore positioned object-shape templates comprises assigning each cell inthe MPS model except for background facies cells to a specific objectshape template positioned within the model at its optimally correlatinglocation.
 5. The method according to claim 1, wherein assigning to eachof the one or more positioned object-shape templates comprises assigningeach cell in the MPS model except for background facies cells to anobject event number which starts at an index of one for each facies,wherein lower objects event numbers indicate objects that better fit theMPS simulation.
 6. The method according to claim 1, wherein determiningwhich cells are to be left unedited and which cells are available forediting comprises: obtaining, using the computer system, a faciesprobability cube either by using a probability cube used in the creatingof the MPS model or using a 3D smoothing filter in each cell for eachfacies; inputting parameters, into the computer system, the parametersincluding order of the facies such that a subsequent facies has agreater continuity than a preceding facies, and a maximum faciesprobability is unmodified; and editing all cells outside of conditioningdata locations in which facies probabilities for each facies, taken inorder of increasing continuity, is greater than the maximum faciesprobability to leave unmodified.
 7. The method according to claim 6,further calculating, using the computer, relative facies probabilitiesor target facies proportions from the facies probabilities.
 8. Themethod according to claim 7, wherein the calculating comprisescalculating the relative facies probabilities or target faciesproportions from the facies probabilities by dividing by the maximumprobability for each facies.
 9. The method according to claim 6, furthercomprising initializing the editing by setting the edited MPS model to anon-edited original MPS model.
 10. The method according to claim 1,further comprising, if matching target facies proportions is needed, foreach facies: creating a list of events with hard data of all objectevent references that are assigned to model cells that containhard-data; calculating a total number of required cells of facies in thefinal model to match the target facies proportion; and calculating anadditional number of cells to be assigned to facies, the additionalnumber of cells being equal to a difference between the target faciesproportion and a length of the list of events with hard data.
 11. Themethod according to claim 10, further comprising, if the additionalnumber of cells is equal to or greater than zero, then: calculating thenumber of cells assigned to each object event number which is not in thelist of events with hard data; calculating a cumulative number of cellsas a function of event number; and finding an event number cutoff suchthat the cumulative number of cells with event number less than eventnumber cutoff is approximately equal to the additional number of cellsto be assigned to facies.
 12. The method according to claim 10, furthercomprising, if the additional number of cells is less than zero, then:calculating the cumulative number of cells assigned to a facies as afunction of the vertical depositional coordinate, wherein a verticalcoordinate of zero is at the base of the object; and finding thevertical depositional coordinate cutoff such that the cumulative numberof cells with vertical depositional coordinates less than the verticaldepositional cutoff is approximately equal to the additional number ofcells to be assigned to facies.
 13. The method according to claim 12,further comprising repeating, for each facies taken in the order ofincreasing continuity, visiting all cells with the additional cutoffconstraints of event number cutoff or vertical depositional coordinatecutoff.
 14. The method according to claim 1, wherein the training imagegeneration parameters comprise object shapes, orientation, position, orsize, or any combination thereof.
 15. The method according to claim 1,wherein using the MPS method includes building a grid in a desiredgeological region and interpreting facies at specific cells within thegrid.
 16. The method according to claim 15, wherein the MPS methodcomprises building a training image using object-based models.
 17. Themethod according to claim 15, wherein the MPS method further comprisesperforming an MPS simulation to obtain a simulated image showing a MPSfacies model.
 18. The method according to claim 1, wherein generatingthe one or more object-shape templates comprises using one or moreobjects within the training image that are isolated from other objectswithin the training image when the training image is generated with morethan one object.
 19. The method according to claim 1, wherein thepositioning of the one or more generated object-shape templates withinthe MPS model comprises convolving the one or more object-shapetemplates with the MPS model to produce a convolution score volume foreach of the one or more object-shape templates.
 20. The method accordingto claim 19, further comprising normalizing the convolution score by itsstandard deviation.
 21. The method according to claim 20, wherein thepositioning comprises finding a local maximum of the convolution score,and positioning an object-shape in the one or more object shapes withinthe MPS model according to its local maximum.
 22. The method accordingto claim 19, wherein the convolving comprises using one or more 2Dobject-shape templates instead of one or more 3D object-shape templates,the one or more 2D object-shape templates being created by projectingthe one or more 3D object-shape templates onto a map-view slice of theMPS model.
 23. The method according to claim 1, wherein assigning toeach of the one or more positioned object-shape templates the uniqueevent reference comprises assigning to each of the one or morepositioned object-shape templates a unique event number.
 24. The methodaccording to claim 23, wherein assigning to each of the one or morepositioned object-shape templates the unique event number comprisesidentifying object shapes within the MPS model that best match eachfacies by using 2D or 3D normalized convolution scores.
 25. A computersystem for implementing a hybrid method for combining multipointstatistic and object-based methods, the computer system comprising: acomputer readable memory configured to store input data comprisingconditioning data, constraints, and training-image generationparameters; and a processor configured to read input data including theconditioning data and constraints and the training-image generationparameters to: create a multi-point statistics (MPS) model using a MPSmethod that satisfies conditioning data and constraints in which themulti-point statistics are derived from a training image created usingtraining-image generation parameters; generate one or more object-shapetemplates of a 2D or 3D object-shape and depositional or structuralcoordinates of each facies type using the training image generationparameters; position the one or more generated object-shape templateswithin the MPS model such that the one or more generated object-shapetemplates maximally correlate to the MPS model; assign to each of theone or more positioned object-shape templates a unique event referenceand assigning the same unique event reference to cells within or in thevicinity of each corresponding object-shape template; determine whichcells are to be left unedited and which cells are available for editing;and assign the cells that are available for editing to facies if thecells that are available for editing are contained by a faciesobject-shape template positioned within the MPS model at its optimallycorrelating location.
 26. The computer system according to claim 25,wherein the processor is configured to assign a limited number ofobject-shape templates or a limited part of each object-shape templatewhere the limited number of object-shape templates or the limited partof each object-shape template allows matching target facies proportion.27. The computer system according to claim 25, wherein the processor isconfigured to assign each cell in the MPS model except for backgroundfacies cells to an object event number which starts at an index of onefor each facies, wherein lower objects event numbers indicate objectsthat better fit the MPS simulation.
 28. The computer system according toclaim 25, wherein the processor is configured to: obtain a faciesprobability cube either by using a probability cube used in the creatingof the MPS model or using a 3D smoothing filter in each cell for eachfacies; receive parameters including order of the facies such that asubsequent facies has a greater continuity than a preceding facies, anda maximum facies probability is unmodified; and edit all cells outsideof conditioning data locations in which facies probabilities for eachfacies, taken in order of increasing continuity, is greater than themaximum facies probability to leave unmodified.
 29. The computer systemaccording to claim 25, wherein the processor is configured to: create alist of events with hard data of all object event references that areassigned to model cells that contain hard-data; calculate a total numberof required cells of facies in the final model to match the targetfacies proportion; and calculate an additional number of cells to beassigned to facies, the additional number of cells being equal to adifference between the target facies proportion and a length of the listof events with hard data.
 30. The computer system according to claim 25,wherein the training image generation parameters comprise object shapes,orientation, position, or size, or any combination thereof.
 31. Thecomputer system according to claim 25, wherein the processor isconfigured to use one or more objects within the training image that areisolated from other objects within the training image when the trainingimage is generated with more than one object.
 32. The computer systemaccording to claim 25, wherein the processor is configured to convolvethe one or more object-shape templates with the MPS model to produce aconvolution score volume for each of the one or more object-shapetemplates.
 33. The computer system according to claim 25, wherein theprocessor is configured to assign to each of the one or more positionedobject-shape templates a unique event number.
 34. The computer systemaccording to claim 33, wherein the unique event number comprisesidentifying object shapes within the MPS model that best match eachfacies by using 2D or 3D normalized convolution scores.